The limitations, drawbacks and/or disadvantages of technologies are as follows: Search Engines are based on Boolean algebra eigenvector algorithms that are used to parse and filter information indices until the top page ranks are determined and displayed to the end user. Unfortunately, some specific keywords combinations may be too narrow and confound a search by hiding optimal results. Search Engines are predominately configured to perform static search patterns. Each search is processed from the ground up without taking into account many requests belonging to one reply. A session consists of consecutive related and unrelated search requests to reach the final destination.
The Internet environment or (U) can be construed as a complex and massive volume network with billions. The Search engine supercomputer analyzes the billions of unique web pages, and then uses eigenvectors to determine the highest ranked pages from the end user's match criteria. As explained, in related subject matters “As the size of the environment increases the level of redundancy and tax burden of a system exponentially increases”.
Transform Data: The supercomputer system cleans, standardizes and organizes the spaghetti of the environment by organizing the huge amount of information in a managerial hierarchical structured supercomputer (hereinafter referred as HIVE) that removes redundancy, latency and the tax burden.
Synchronize tasks: the HIVE is a decentralized supercomputer consisting of a plurality of nodes, which are specifically arranged in three tiers. The summit tier coordinates and executes global tasks. The middle tier coordinates and executes regional tasks. The lower tier coordinates and executes localized tasks and processes the lion share of noncritical transactions. The summit node of each tier synchronizes tasks by sending command messages that assigns the fuzzy logic state of each node belonging to its chain of command.
Lateral and Vertical Synergy: A tier consisting of groups of nodes that are independent from other groups of nodes. Each tier partition performs mission critical tasks within their domain and works in parallel with other partitions of the same tier. Each node can shunt available resources using lateral and vertical synergy with parent, sibling or subordinate nodes to maximize available resources. Each node continuously analyzes its own environment current conditions and forward chains summary information until reaching the summit. At this point, the summit nodes rearward chain messages with command instructions that priorities resources, and notify each subordinate with command instructions tasks constraints taking into account present network conditions to avoid saturation, clog and eliminate the tax burden of the environment’.
Remove chaos and anarchy: Once the ‘spaghetti of the environment’ is eliminated the HIVE creates command messages that are also known as environment bitmap data. Command messages coordinate and synchronize each node to operate at maximal output capacity. Each node operates without adversely affecting the network flow of data. The HIVE maximizes available throughput and limits the exponential rate of growth of complexity as the size of the environment increases.
Convert Requests into Ideas: Search Engines dependency on Boolean algebra use inductive reasoning popularity scores to find the top results. In contrast, the HIVE using deductive reasoning to interpret interactive input as being part of an idea being formulated by both the left and the right sides of the brain. When a request is incomplete the HIVE probabilistically supplies and inserts missing gaps of information. Related art teaches that a Vector CDR can be expressed as the summation of a plurality of valid vectors. The HIVE correlates a plurality of partial vectors and creates a resultant vector containing the top (n) pages.
Heartbeat: the Summit tier gives a heartbeat to the HIVE.
The X_FOB and Y_CDIF Inventory Control Bitmaps are commonly known and referred as managerial relationship indices summary results, with pre-calculated look up information to eliminate redundant and resource intensive calculations.
Brief Summary of Applicant's Related Applications
Search Engines use Boolean algebra and eigenvector algorithms to parse and filter information indices until the top page ranks are determined and displayed as output. Unfortunately, some specific keywords combinations may be too narrow and confound a search by hiding optimal results. Search Engines are predominately configured to perform single static search patterns. Each search is processed from the ground up without taking into account many requests belonging to one session. A session consists of consecutive related and unrelated search requests while randomly surfing the web.
The Optimizer in parallel keeps in existence for each search pattern its corresponding virtual simulation environment that contains all relevant bound pages. Each virtual simulated environment possesses a relative Master Index. The Optimizer continuously purifies and synchronizes the plurality of relative Master Index that permits to match/merge and then correlate the Internet's Master Index in real time.
The Optimizer continuously scans and detects the environment in real time for new content with significant difference quality to update each search pattern's virtual environment partition relative Master Index and associated collections of top (n) pages. The Optimizer heuristically reads the content of each page, paragraph, sentence, and term clusters. Existing Master Index has an absolute rank value for each page.
The Optimizer rank value is dynamically adjusted by matching independent variables and related keywords belonging to the search pattern to generate a content value. The Optimizer “cherry picks” the best content value web pages as output. The output is forward chained back to the end user's terminal and displayed.
The Optimizer is a method and system for simulating Internet browser search capacities that cleans, standardizes, organizes, and transforms the massive amount of data into a lingua franca comprising of valid keywords, term clusters, and unique geospatial patterns contained in the Internet collectively known as patterns that exist in page. The comprehensive collection of search patterns with their relative Master Index are stored and continuously updated as web crawlers detect significant changes in the environment.
Each Search Pattern consists of at least one independent variable, e.g. (I), (J), (K), (X), (Y) and (Z). Search Patterns with 0 independent variables use randomly surfing the web techniques that find the final destination within the massive (U) or Internet environment.
Related Applications (U.S. patent application Ser. No. 10/926,446)
Partial Differential Equation Vectors Model: Solves solutions with two or more independent variable. The solution requires an equation with a plurality of independent variables. Thus we replace the traditional vector with Partial Differential Equation Vectors.
E.g. Using Set Theory, the telecommunications environment U can be divided into three independent networks: Fixed (X), IP Telephony (Y) and Wireless (Z). A Simple Call exists when the call uses a single network (X, Y or Z), whereas a Complex Call exists when the call must use more than one independent network environment to complete the call.
E.g. A call uses three different networks Fixed, IP Telephony and Wireless (I, J, K), each independent variable solves the billing entity and resultant vector for the call. The Switch controlling the call uses its Partial A and Partial B functions to create a final resultant vector that includes all the circuits belonging (I, J, K) for just one call. Yes, they are three independent calls one per network which is billable, yet in fact there is only one call.
Related Applications: (U.S. patent application Ser. No. 10/852,394)
Computer Network System: consists of a plurality of nodes, where each one is programmed with Artificial Intelligence to perform predefined tasks that are logistical rationalized based on the current conditions of the environment. The computer network system is synonymous with Superset (U). The cluster is divided into three geospatial tiers: a) Global, b) Regional, and c) Local. Each tier has multiple functionalities such as a) provisioning, b) Total Quality Management or (TQM), c) Data Manipulation, d) Management Information Systems (or MIS), e) Expert Information Systems (or EIS) and f) Inventory Control.
Computer Network System Nodes: All nodes are autonomous and in real time analyze, evaluate, gather and process information from the environment. From incipiency upon receiving the fuzzy logic piece of information that triggers a new task or update pending activities. Each node is assigned to Superset(I), Set(I, J), or Subset(I, J, K) cluster tier, and to the geospatial domains (X) or global, (Y) or regional, and (Z) local to map independent variables (I, J, K, X, Y, Z) that build the managerial hierarchy as follows:
Managerial Hierarchy: The Summit Tier allows users to have access to their information in real time. The Middleware Tier geographical manages physical warehouses. The Lower Tier controls a plurality of points of presence belonging to 3rd parties and collectively constitutes the workhorse of the system.
Node Synchronization and Buffer Resources: Every predefined cycle each node synchronizes the latest inventory. Nodes request siblings for any excess buffer resources to complete a task using vertical and lateral synergy. Parent nodes use their chain of command to coordinate their subordinates. Thus, all nodes synergistically collaborate to process tasks and collectively mimic a global online supplier.
Eliminates the Spaghetti Phenomena: The global online supplier gathers, distills, analyzes and then standardizes raw information into primed lingua franca data so that Information Certainty is achieved and thus removes the chaos and anarchy or Spaghetti Phenomena.
Primes Vector CDR: Lingua franca messages are Vectors and contain the vector trajectory and all transactional segments information. Legacy systems send all transactional segments to centralized billing data warehouses that match/merge each transactional component and then correlate the information into a billing entity. Whereas the computer network uses artificial intelligence to assign a hierarchical owner and plots circuit by circuit the vector trajectory and only activates relevant nodes to the transaction so that nodes can communicate amongst themselves via forward and reward chaining. Nodes send all dynamic and fixed costs to hierarchical owner so it can correlate the rated billing entity absent of a centralized billing data warehouse.
Avoids Taxing the Throughput: The computer network system monitors the limited resources and capacities of the network to avoid taxing available throughput in real time. Each node can create, plot and update resources as soon as new relevant messages from the environment are detected.
Uses Synergy to Maximize Throughput: Upon receiving environment command instructions each node can manage the flow of information of their subordinates from predefined point A to point B routes to avoid saturation. The computer network maximizes throughput by permitting each node via synergy to shares resources with other nodes that have substantial buffer resources to eliminate the tax burden and waste.
Analyzes Network Traffic: Network traffic is analyzed as the informational traffic is measured based on the latest command instructions and known routing throughput limitations of each given domain. The summit nodes of each tier perform the nonobvious task synchronizing and managing their subordinates to use synergy to minimizing waste before permitting data to be transmitted through their chain of command.
Computer Network System Reaches Informational Certainty: Nodes remove waste at incipiency one transaction at a time, so that the computer network system can be considered a real time invention.
Computer Network System Stabilizes the Flow of Information: Summit and Middleware nodes stabilize the flow of information and update the XLDB database with trending statistics used to optimize resources and available bandwidth. Each node of the managerial hierarchical synergy works in parallel with others nodes to work as a single unit permitting the computer network to create a virtual instance of the organizational environment.
Computer Network System is a Real Time System: Once the ‘Spaghetti Phenomena’ is eliminated, informational certainty is achieved removing the need for a central mainframe. Consequently, a real time solution consists of synergistically synchronizing all the computer network system functions.
Computer Network System Evaluates Network Resources: Each node has its own location identification means and must be assigned to one geospatial specific domain cluster such as local, regional or global. Each activity and task is processed in parallel, starting from the point of origin and ending at the point of destination. The computer network system rearward chains the routing vector information through the simulation network to the point of origin and analyzes and evaluates the best usage of network resources.
Related Applications (U.S. patent application Ser. No. 11/584,941/Issued U.S. Pat. No. 7,809,659)
XCommerce, Deductive Reasoning Supercomputer: is a method that simulates the entire superset of potential valid interactive input regular expression requests construed during an Internet browser search and converting the results set into a vector based statistical data that enable efficient and accurate searching. XCommerce simulates, standardizes and partitions the Internet into a plurality of concurrently working environment using a Managerial hierarchical method of indexing and searching as follows:
Managerial Hierarchical Index Relationships: a request is broken down into keywords and clusters, and then converts them into a search pattern that optimally minimizes the quantity of relevant pages.
Determining what is Relevant and Irrelevant: Pages that match a Relationship Index are relevant, and those that do not are irrelevant. Irrelevant web pages are discarded completely from analysis.
Partition the Environment into Blocks: the environment is subdivided into a plurality of blocks that are arranged based on Managerial Hierarchical levels as follows:
Each Search Pattern restricts the geometric rate of growth of the Internet environment by creating the relevant environment that is used by all managerial relationship levels when purifying the search process.
The Internet environment is considered a Super Block and is partitioned into a three level Managerial Hierarchy. First: the primary index relationship creates Blocks that maps an improved environment. Second: the secondary index relationship creates Sub Blocks that maps an optimal environment. Third: the tertiary index relationship creates Mini Blocks that maps an optimal solution.
Identifies Static Search Patterns: the computer network system determines if the search pattern already exist and if yes obtains the top (n) pages from the databases and sends the output to the end user.
Calculates Dynamic Search Patterns: uses managerial hierarchical relationship indices to create optimal size partitions and compares remaining key featured associations to determine if they match against the content of the top (n) pages. When a match occurs each page is gain factored by each key featured association vector value and then the Optimizer picks the top (n) pages with the highest values.
Finds New Search Patterns: stores each new search patterns and top (n) pages.
Displays Top (n) pages: Sends and displays the output to the end user's terminal.
Related Applications (U.S. patent application Ser. No. 12/146,420/Issued U.S. Pat. No. 7,908,263)
A search engine optimizer, hereinafter referred as Cholti, gathers interactive input from a browser. The optimizer reorganizes the interactive input as optimal input that is sent to the search engine, and then the output is sent to the end user. Each request is converted into a search pattern and stored as a mathematical equation that mimics the left (linguistics) and right (geospatial) side of the brain.
Related Applications (U.S. patent application Ser. No. 12/764,934)
Lottery Mathematics: Cholti and XCommerce teaches how to improve accuracy of a requests by using independent variables (I, J or K) to map and create managerial hierarchical partitions of the Internet environment such as: from top to bottom Superset(I), Set (I, J) and Subset (I, J, K) datasets. For this application Lottery Mathematics is hereinafter referred to as Logic Mathematics.
Hot and Cold analysis: uses logic mathematics to estimate the size of the environment as the end user types interactive input and assigns primary independent variable (I) to the filter with the following formula: (x!−(x−6)!)/6! E.g. the number of permutations for a 10 number draw is (10!−4!)/6! 4!=24, 6!=720 and 10!=3,628,800. (3,628,800/24)/720=210 permutations. Thus, each grid has 1/210 in being the outcome. The English language estimated Master Index size of the environment in the year 2013 is Logic—305 Basis or 1,099,511,627,776 or (2^40) pages hereinafter for simplicity 1 trillion.
E.g. the number of permutations for a 305 number draw is 1 trillion or 305!−(305−6!/6! The quality of the Glyph that represents (I) or primary index relationship determines the Mass. E.g. If the keyword Civil=(I) the Mass=1, and if cluster “American Civil War”=(I) the Mass=2.
TABLE 1Size of environment based on Massa. Mass = 0 (Logic_305_Basis = 1 trillion) or 305! − (305 − 6)!/6!b.Mass = 1 (Logic_100_Basis = 1,192,052,400) or 100! − (100 − 6)!/6!c. Mass = 2 (Logic_70_Basis = 131,115,985) or 70! − (70 − 6)!/6!d.Mass = 3 (Logic_50_Basis = 15,890,700) or 50! − (50 − 6)!/6!e. Mass = 4 (Logic_40_Basis = 3,838,380) or 40! − (40 − 6)!/6!f. Mass = 5 (Logic_30_Basis = 593,775) or 30! − (30 − 6)!/6!g.Mass = 6 (Logic_20_Basis = 38,760) or 20! − (20 − 6)!/6!h. Mass = 7 (Logic_15_Basis = 5,005) or 10! − (10 − 6)!/6!i. Mass = 8 (Logic_6_Basis = 1) or final destination.
I. Simulating the Human Brain:
Human Brain: Each linguistic Glyph is assigned to the [L] left side of the brain and each geospatial Glyph is assigned to the [R] right side of the brain and the Anchor is the best common denominator Glyph.
The Dominant Tendency of each request is given a [L] linguistic, and [R] geospatial tendency, and then Cholti reorganizes, maps and plots the Glyphs to create a Managerial Hierarchical Relationship Index.
Human Brain Intelligence: transforms each Search Pattern and identifies independent variables based on mass partitions of the Internet and creates Join, Simple, Hybrid, Complex and Optimal Pyramids.
Human Brain Wisdom: analyzes the top (n) pages and expands each [AX], [BX] and [CX] Glyph equation with key featured association dependent variables.
Cholti picks one of four Search Strategies: [LL], [LR], [RL], and [RR], which have different set of business rules to analyze the Internet and limits partitions not to exceed 1 billion or (2^30) pages and thus eliminates the principal confounding variable, which is the exponential rate of growth of the environment.
E.g. the environment grows geometrically to 20 billion, or 40 billion or 100 billion or 1 trillion pages, but once the Dominant Tendency is selected the relevant environment will always be a Logic—100_Basis or 1,192,052,400 pages, while making most pages irrelevant.
II. [L+R] Managerial Relationship Events
If the independent variable (I) is represented by the Historical Event “American Civil War” {1863}, where “American Civil War” is the left side of the brain variable (I) and 1863 is the right side of the brain (X), and are merged to a Single Event or Superset(I!) with Mass=3. The Double Event or Set(I,J) !! with Mass=5 and (I, J, X, Y) independent variables, and finally for Triple Event or Subset (I, J, K)!!! with Mass=8 consisting of [L] left side of the brain (I, J, K) and [R] right side of the brain (X, Y, Z) independent variables.
First Significant Event or (FSE): is a vague search that maps an improved environment. The Internet environment (a, b, c, d, e, f) becomes the improved environment (FSE, b, c, d, e, f) for Superset(I) dataset. Hereinafter Lucky numbers are replaced with IV that is the abbreviation for Independent Variables.
TABLE 2FSE Size of environment based on Massa. Mass = 1 (Logic_100_IV_1 or 75,287,520) or 100! − (100 − 5)!/5!b.Mass = 2 (Logic_70_IV_1 or 12,103,014) or 70! − (70 − 5)!/5!c. Mass = 3 (Logic_50_IV_1 or 2,118,760) or 50! − (50 − 5)!/5!
Second Significant Event or (SSE) is a concise search that maps an optimal environment. The Internet environment (a, b, c, d, e, f) becomes the optimal environment (FSE, SSE, c, d, e, f) for Set(I, J) dataset.
TABLE 3SSE Size of environment based on Massa.Mass = 1 (Logic_100_IV_2 or 3,921,225) or 100! − (100 − 4)!/4!b.Mass = 2 (Logic_70_IV_2 or 916,895) or 70! − (70 − 4)!/4!c.Mass = 3 (Logic_50_IV_2 or 230,300) or 50! − (50 − 4)!/4!d. Mass = 4 (Logic_40_IV_2 or 91,390) or 40! − (40 − 4)!/4!e. Mass = 5 (Logic_30_IV_2 or 27,405) or 30! − (30 − 4)!/4!
Third Significant Event or (TSE) is a precise search that maps an optimal solution. The Internet environment (a, b, c, d, e, f) becomes the optimal solution (FSE, SSE, TSE, d, e, f) for Subset(I, J, K) dataset.
TABLE 4TSE Size of environment based on Massa.Mass = 1 (Logic_100_IV_3 or 161,700) or 100! − (100 − 3)!/3!b.Mass = 2 (Logic_70_IV_3 or 54,740) or 70! − (70 − 3)!/3!c. Mass = 3 (Logic_50_IV_3 or 19,600) or 50! − (50 − 3)!/3!d. Mass = 4 (Logic_40_IV_3 or 9,880) or 40! − (40 − 3)!/3!e. Mass = 5 (Logic_30_IV_3 or 4,060) or 30! − (30 − 3)!/3!f.Mass = 6 (Logic_20_IV_3 or 1,140) or 20! − (20 − 3)!/3!g.Mass = 7 (Logic_15_IV_3 or 445) or 10! − (10 − 3)!/3!
Fourth Significant Event or (QSE) is an optimal search that maps the optimal answer. The Internet environment (a, b, c, d, e, f) becomes optimal answer (FSE, SSE, TSE, QSE, e, f) as follows:
TABLE 5QSE Size of environment based on Massa.Mass = 1 (Logic_100_IV_4 or 4,950) or 100! − (100 − 2)!/2!b. Mass = 2 (Logic_70_IV_4 or 2,415) or 70! − (70 − 2)!/2!c.Mass = 3 (Logic_50_IV_4 or 1,225) or 50! − (50 − 2)!/2!d. Mass = 4 (Logic_40_IV_4 or 780) or 40! − (40 − 2)!/2!e.Mass = 5 (Logic_30_IV_4 or 435) or 30! − (30 − 2)!/2!f. Mass = 6 (Logic_20_IV_4 or 190) or 20! − (20 − 2)!/2!g.Mass = 7 (Logic_15_IV_4 or 45) or 10! − (10 − 2)!/2!
Gamma Functions
Cholti and XCommerce teach how to create search patterns that improve the accuracy of a request. The Likely and Unlikely analysis uses Gamma functions to solve for the size of the environment.
E.g. the end user types 1863 American Civil War, which the Optimizer automatically maps the [L] left side of the brain term cluster “American Civil War” with [R] right side of the brain geospatial keyword to create “American Civil War”. The “War between the States” is also synonymous with the American Civil War, and thus “between the” which are dependent variables since they have a Mass less than 1. The Dominant Tendency and the keyword “States” which has a Mass of 1+ is Likely. Let us assume, the keywords {1861, 1862, 1864 and 1865) are Unlikely. The Likely and Unlikely Gamma function values are as follows: “American Civil War” {1863}=50!−(50−5)!/5! or 2,118,760 pages. Plus “States” Likely Analysis:=49.9!−(49.9−5)!/5! or 2,096,762 pages. Plus Unlikely Analysis:=49.86!−(49.86−5)!/5! or 2,088,014 pages.
Search Pattern Variables
Independent Variables: The IV Numbers are the control variables or independent variables that determine the Circle of Accuracy, which in turn limit the size of the environment.
Dependent Variables: The Dependent Variables (hereinafter DV) Numbers are the observable variables or dependent variables, and are considered strong filters.
Complement Variables: The Complement Variables (hereinafter CV) Numbers are the measured variables or dependent variables, and are considered weak filters.
TABLE 6Gamma function adjustment of the Logic Basisa.Independent/Control Variables (IV Numbers)+1.00b.Dependent/Observable Variables (DV Numbers)+0.100c. Dependent/Complement Variables (CV Numbers)+0.010
Partial Differential Equations: When using Partial Differential Equations usually the solution is not unique due to the fluid and dynamic conditions of the search process, and ergo input combination usage behavior directly affects the size of the environment (or boundary of the region) where the solution is defined.
Related Applications (U.S. patent application Ser. No. 13/247, 964)
Simulation Comparison
‘Boolean Algebra: End user types the input “Napoleon” or (i) and the inductive reasoning search engine assigns a “1” when a match occurs, and sums up the number of unique occurrences which is equal to 8,000,000 pages. Like always there is good, bad and ugly content. Based, on the business model of the search engine companies the lion share of their profit comes from advertisement, we will assign as (j) the commercialization process, which shrinks the environment to 10,000 pages, and the further distill by using the page quality value to create an environment of 100 pages. At this point selecting the top (n) result is really easy, by just automatically selecting the pages with the highest page rank and then send the output to the end user's browser. In this case the top site will always be wikipedia.com since Ask.com and Powerset technologies made a great emphasis of the content quality value of this site and then search engine industry followed. Encyclopedia Britannica is (2) and Encarta is (3) have a 10 in quality value and have a very high page rank
Cholti: determines that “Napoleon” is the anchor and commercial keyword and using the human brain strategy creates an [LL] environment of 8,000,000 pages that possess Super Site (a, b, c) characteristics, which is used to create the Simple Pyramid and [AX] Macro Page that converts “Napoleon” into “Napoleon Bonaparte” and adds “France”, “General” and “Emperor” to the Advanced Glyph equation with magnitude of 7. At this point Cholti uses Super Site (d) actual content characteristics, to create the Hybrid Pyramid and [BX] Macro Page that adds “Austerlitz”, “Waterloo”, “Blucher” and “Wellington”, and “1801 to 1815” to the Improved Glyph equation with magnitude of 10. Cholti now uses Super Site (e) trending characteristics, to create the Complex and [CX] Macro Page that adds a collection of key featured associations to the Optimal Glyph equation with magnitude of 15. Now Cholti performs the ‘Cherry Picking’ process to select the top (n) pages by reading, deciphering, analyzing the actual content.
The Real Difference: Wikipedia.com will always be the optimal web page for the static and vague search, whereas Cholti has three paths (a) (Static Ranking) for concise searches Wikipedia.com is automatically the optimal response, (b) (Actual Content) for precise searches if the end user typed additional keywords, and Cholti determines that Encyclopedia Britannica is the best fit content, then Wikipedia.com is demoted from the automatic 1st position and sent to the 2nd position. (c) (TQM levels of satisfaction) for optimal searches where wikipedia.com had the top spot, but did not satisfy, and after the second request Encyclopedia Britannica had the top spot, and also did not satisfy, for the third request the top responses for the request (1-2) are demoted, and now Encarta Encyclopedia the other high quality content has the top spot. Cholti is dynamic and personalized whereas existing search engines are static. TQM is the heart and soul of the technology and thus customer satisfaction. Yes, accuracy is directly related to the time from beginning to end, and the amount of knowledge and work required from the end user to reach the final destination, Cholti previews the entire content in behalf of the human to minimize TIME and using deductive reasoning to reduce the amount of gray matter required to reach the final destination to maximize satisfaction.
We've overcome these “issues” or greatly improved the search optimally by doing the following: Generally stated end user's requests are converted into the Mayan style Glyphs that have left side and right side of the brain characteristics. The system understands that each request on its own can be optimally satisfying, and also knows that some require trial and error method. To solve this dilemma the optimizer creates Super Glyphs that have weighted value for a plurality of instances within a session.
Cholti Method
Minimally the system needs to be a computer with a database means that store a ‘CORE List’ that consists of statistics for each keyword or cluster performing the following steps:                a. Identify each keyword interactively.        b. Validate each keyword to belong to a group.        c. Verify if a keyword will be an active participant in the process of reducing the size of the environment.        d. Estimate the [AX] or ‘Before’ vague search environment size from the input typed by the end user.        e. Determine if the end user's request is significant.        f. Create Basic Glyphs that best reflects the essence of the [AX] or ‘Before’ request that will permit the creation of a hierarchical set consisting of a plurality of valid Superset(I), Set(I, J) and Subset (i, j, k).        g. Reorganize the end user's request to create Advanced Glyphs that further distills and shrinks the size of the environment using the [BX] or ‘After’ request.        h. Recognize Advanced Glyphs to determine if it already exists in the ‘CORE List’. If the Advanced Glyph exists in the ‘CORE List’ the output is readily available and preprocessed no further calculations are required. Otherwise, the system must continue with [CX] or ‘Improved’ and [DX] or ‘Optimal’ samples.        i. Request the server to perform the ‘Improved’ sample by hierarchical distributing the search amongst subordinate based on ownership of the primary, secondary and tertiary keyword or term cluster. The Basic and Advanced Glyphs are used to assign size parameter to each valid set of the hierarchical set.        j. Adjust dynamically the value of each keyword and term cluster.        k. Exclude identified Zero Cluster keywords.        l. Emphasize through rules of association and transitivity a plurality of requests that are considered to have common denominator elements and are then correlated into a partial environment. The partial environment consists of a plurality of request. The partial environment retains the characteristic of each individual request.        m. Deemphasize unrelated keywords to the last significant end user's request. This process is also known as Mulligan and is uses set theory to determine the relationship between input and the last significant request.        n. Maximize keyword values by using the Hot Algorithm that measures the usage pattern and significance of a keyword in a session.        o. Minimize keyword values by using the Cold Algorithm that weights keyword irrelevancy. ‘Zero Clusters’ and unrelated keywords have a reasonable probability of hiding the optimal result.        p. Correlate at least one partial environment into the [CX] or ‘Improved Samples. This process draconically reduces the environment size using Hot & Cold Algorithm parameters and stores the essence of the matter into the Super Site of each valid and visible page.        q. Assign a corporate signature to each Super Site.        r. Pick the small [CX] Sample top results of each hierarchical set to generate a collection of Super Pages.        s. Distill the small [CX] Sample using geospatial dimensions that have exact or estimated latitude and longitude components such as Country, Region, LATA, Zip Code, IP Address and ANI.        t. Commercialize keywords if the already exist in the Commercial Glyph database.        u. Deciphers, analyzes the actual content (gray matter), measures TQM level of satisfaction (trending) of each page in order to pick using reasoning the [DX] or optimal sample.        v. Translate the interactive input into a Cholti language Super Glyph equation.        w. Respond with the output or optimal response. The output may be identified as already existing in the preprocessed ‘CORE List’ in step h) “recognize all preprocessed calculations in the search pattern database. New search patterns not found in the search pattern database perform steps i) “request” to v) “translate”.        x. Display to the end user the output or optimal request. The formatted output is considered an object.        y. Recalculate each time the “optimal button” is clicked in the web browser and significant difference event is estimated compared to the latest Super Glyph equation or partial environment.        z. Consolidate a plurality of partial environment into a resultant environment that is contained with the valid environmental size of the hierarchical set.        
Cholti Triangulates the Search Process
1st transforms vague searches into Super Glyph ideas and simulates the human brain to assign a search strategy [LL], [LR], [RL], and [RR] and anchor or commercial cluster and employees independent variable (I) to create the improved environment with 1,000,000 pages, and thus eliminates independent variable (I) from any further calculation.
2nd amends vague searches into concise searches employing rules of association and relevance to create the optimal environment with 10,000 pages, and thus eliminates independent variable (J) from any further calculation.
3rd improves concise searches into precise searches and then measures the actual content based on likeness and trending to create the Optimal solution with 100 web pages, and thus eliminates independent variable (K) from any further calculation.
4th Ameliorates precise searches into optimal searches and then ‘Cherry Pick’ the actual content to create an optimal environment of the top response, and also expands the Search Pattern Super Glyph equation when changing the environment.
In conclusion static and vague searches use the Internet environment with billions of web pages. Cholti converts the vague search into static or dynamic Glyph equations that create a Search Pattern that is best described as a managerial hierarchical informational pyramid object as follows:
1st maps English language input to the left brain equation and geospatial input to the right brain equation, and the determines the dominant tendency of the brain to assign a Search Strategy to create a Join Pyramid or Super Block that maps a relevant environment with 1 billion pages as the lowest level of informational certainty.
2nd utilizes the anchor Glyph and Commercial Glyph to purify and shrink the size of the environment, and the uses the primary independent variable (I) to changes the vague search into a concise search that creates a Simple Pyramid or Block that maps an improved environment with 1,000,000 web pages that replaces and eliminates the primary independent variable (I) from further calculation. Assigning the Simple Pyramid with a partial master index.
3rd uses independent variable (J) to changes the concise search into a precise search that creates a Hybrid Pyramid or Sub Block that maps an optimal environment with 10,000 web pages that replaces and eliminates the secondary independent variable (J) from further calculation. Assigning the Hybrid Pyramid with a partial master index.
4th uses independent variable (K) to changes the precise search into an optimal search that creates a Complex Pyramid or Mini Block that maps an optimal solution with 100 web pages that replaces and eliminates the tertiary independent variable (K) from further calculation. Assigning the Complex Pyramid with a partial master index.
5th Cherry picks the optimal solution using checkmate combination independent variables finds the Optimal Pyramid with the final destination.
Final clarification when independent variables are eliminated from further calculation they create higher tiered Informational Pyramid Structure objects as the informational certainty improves as follows:                a. Each Search Strategy eliminates the geometric growth of the Internet and binds a vague search into a Join Pyramid that maps a relevant environment with 1 billion pages.        b. The primary independent variable (I) changes the vague search into a concise search and creates the Simple Pyramid that maps an improved environment with the top 1,000,000 web pages.        c. The secondary independent variable (J) changes the concise search into a precise search and creates the Hybrid Pyramid that maps an optimal environment with the top 10,000 web pages.        d. The tertiary independent variable (K) changes the precise search into an optimal search and creates the Complex Pyramid that maps an optimal solution with the top 100 web pages.        e. The Simple Pyramid filters exclusively the relevant environment with the primary independent variable (I) and thus the Superset(I) mathematical notation. The Internet absent of (I) relevant to the search.        f. The Hybrid Pyramid filters exclusively the improved environment with secondary independent variable (J) and thus the Set(I, J) mathematical notation. The Internet absent of (I, J) relevant to the search.        g. The Complex Pyramid filters exclusively the optimal environment with tertiary independent variable (K) and thus the Subset(I, J, K) mathematical notation. The Internet absent of (I, J, K) relevant to the search.        h. 1st Simple Pyramid mapped the improved environment and expands the mathematical Glyph equation by adding the 1st key featured associations. 2nd: Hybrid Pyramid mapped the optimal environment and expands the mathematical Glyph equation by adding the 2nd key featured associations. 3rd: Complex Pyramid mapped the optimal solution and expands the mathematical Glyph equation by adding the 3rd key featured associations.        i. The ‘Cherry Picking’ process uses the checkmate combination variables to find the final destination by using the inductive reasoning popularity score and the deductive reasoning actual content score.        j. The final destination and top (n) pages are sent to the end user's browser as output.        